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# Software Developer (Java) - Consultant

- **Company:** Resourcesoft, Inc.
- **Location:** United States
- **Experience:** Experienced
- **Contract:** Contract
- **Skills:** Java (Programming Language), Airflow, Amazon Web Services, Microsoft Azure, Cloud Computing, Cloud Database, Cloud Engineering, Databases, Data Architecture, Information Engineering, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Warehousing, Python (Programming Language), PostgreSQL, Operational Databases, Performance Tuning, Cloud Services, Data Mesh, Software Engineering, SQL Databases, Data Processing, Snowflake, Data Lakes, Complex Event Processing, Integration Frameworks, Data Management, Domain Driven Design
- **Published:** November 7, 2026
- **Apply:** https://www.dice.com/job-detail/d37b04d6-55c1-4c8c-a17a-c2101eddb726

## About the Role

* 4 years or more of data engineering experience spanning system design, data architecture, application development, and cloud data solutions.
 * Proficiency in Java, Python, SQL, AWS, Azure, ETL/ELT, and modern data processing technologies.
 * Experience with Snowflake, Amazon Athena, PostgreSQL, cloud-native databases, data warehouses, and data lakes.
 * Experience with Airflow, event processing, data integration, data mesh, domain-driven design, and reusable data frameworks.
 * Experience in data modeling, architecture frameworks, performance optimization, technology evaluation, and production stability.
 * Excellent verbal and written communication skills.

## Description

* Design and implement scalable data platforms, data lakes, data warehouses, and cloud-based analytical solutions.
 * Develop production data solutions using Java, Python, SQL, Airflow, and cloud-native data technologies.
 * Create data models and architectural artifacts supporting integration, analytics, and enterprise data initiatives.
 * Build reusable data frameworks, pipelines, integrations, and services using modern data engineering patterns.
 * Evaluate emerging technologies and define target-state approaches for scalable, maintainable data architectures.
 * Optimize data platforms, resolve performance issues, and support reliable production operations across data environments.